To me, using AI to develop applications based on traditional code feels like attaching a jet engine to a horse cart. But let’s start from the beginning.
AI Hype vs. Developer Reality
Recently I’ve repeatedly seen posts and articles claiming “AI is the new Low-Code/No-Code”. And since I’m currently working on a project that’s focused on low-code development, people keep asking me if that still makes sense considering AI can write code now. There seems to be a widespread sentiment that optimizing development concepts makes no sense anymore - apparently AI solves all development issues. At the same time I come across posts from many developers who are frustrated, because in their daily experience of working with AI, they don’t see it living up to the hype and the rising expectations that come with it. Maybe they are all just not good at writing prompts or use the wrong models, but I doubt it. To me it seems obvious that AI increases the need for better development concepts.
The Horse Cart
I’m from Germany and on the German Autobahn (highway) there’s no speed limit. If your car and the traffic allow it, you can drive 300 km/h or even faster. But for that to work well you not only need a good engine, the whole car needs to be designed appropriately. It needs good wheels, good brakes, good steering, …
You can’t just take a horse cart, replace the horse with a jet engine and then think you’ve got an awesome super fast vehicle. Even if you get it to work it will be very bumpy, hard to maneuver and most likely won't end well. It definitely wouldn’t be allowed to drive on the Autobahn. :)
A jet engine could make a horse cart much faster, but should it?
AI can generate lots of code very fast, but should it?
Back to Reality
To be fair, comparing modern programming languages to a horse cart might not be the best fit, perhaps they are more like cars. But attaching a jet engine to a car is still not a great idea. It could be a cool experiment, but not useful for daily life. In a similar way, I think AI coding can work well in some contexts, but for high-level features of most standard applications it’s probably not the best approach.
Current AIs lack real intelligence and creativity - they just do efficient pattern matching, which can be very effective with common patterns. But the more diverse, nested and unconventional the combinations of patterns are, the more insufficient are the results. To efficiently work with AI we still need simple ways to create and fix unconventional things ourselves. Our best source of intelligence and creativity is still our brain. As far as I understand, current AI architectures will always struggle to apply uncommon details. For that we need scalable low-code concepts, because reviewing, fixing and extending lots of AI-generated code is no fun. For AI it’s also not ideal to work with code, especially in an environment with dependencies across several different languages. With a higher level of abstraction and simpler logical structures, AI can work better and is less likely to make mistakes. At least that's what AIs told me. 😁
AI vs. Visual Tools
For many tasks, using specialized tools is more efficient than instructing an AI. AIs are awesome, but not for everything. Examples:
- Moving an element in a UI is easier by drag and drop with a good WYSIWYG editor than telling an AI to do it.
- Adding or removing a step in a workflow is easier with a visual workflow builder than doing it with AI in code. Especially if you don’t know the workflow by heart and first need to ask AI to explain or visualize it.
Ideally we should have good visual development tools and AI combined. For example, use AI to generate a workflow based on some high-level description and then modify details by drag and drop. Visual tools reduce the cognitive complexity for our brain. Often doing a task is the easiest part, figuring out what the system currently does and what needs to be changed can be much more challenging. Even if we end up mainly using AI to apply changes, consistent visual representations are still very helpful for the whole development process.
Conclusion
AI and low-code development shouldn’t be seen as competing approaches, they should complement each other. AI can do most of the groundwork while visual editors help us to efficiently review and modify the results. There are already numerous low-code platforms with AI integration available, but one of the key problems is a common lack of flexibility regarding technical concepts and licensing. I think moving forward we need a simple and flexible abstraction layer on top of code that overcomes the technical and legal limitations of established low-code platforms. In my opinion it's long overdue and with AI it has become even more important.
For more details, how I envision that abstraction layer, you can read this article:
https://ontineo.com
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